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Architects' Tech Alliance
Architects' Tech Alliance
Feb 9, 2025 · Artificial Intelligence

How DeepSeek R1 Replicates OpenAI o1 Using Large‑Scale Reinforcement Learning

The article provides an in‑depth technical analysis of DeepSeek R1, explaining how it reproduces OpenAI o1's reasoning abilities through rule‑based large‑scale reinforcement learning, mixed SFT data, and efficient scaling, while discussing its broader impact on AI model development and capability density trends.

AI industryCapability DensityDeepSeek
0 likes · 19 min read
How DeepSeek R1 Replicates OpenAI o1 Using Large‑Scale Reinforcement Learning
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Feb 8, 2025 · Artificial Intelligence

Analyzing DeepSeek R1 Inference Projects: Source Code, Cold‑Start, and Scaling Techniques

This article examines DeepSeek R1’s three breakthroughs, its low‑cost optimizations that bypass CUDA, and the resulting impact on the AI ecosystem, then provides a detailed technical review of seven open‑source reproductions—Open‑R1, Tiny‑Zero, SimpleScaling‑S1, and simpleRL‑reason—covering their architectures, reinforcement‑learning pipelines, and code implementations.

DeepSeekInference ScalingPTX
0 likes · 10 min read
Analyzing DeepSeek R1 Inference Projects: Source Code, Cold‑Start, and Scaling Techniques
JavaEdge
JavaEdge
Feb 8, 2025 · Artificial Intelligence

Why DeepSeek R1 Rivals ChatGPT o1: Architecture, Training, and Cost Insights

This article provides a detailed technical analysis of DeepSeek's R1 large language model, covering its background, architecture, training methods, hardware optimizations, performance claims, user impressions, deployment options, and the challenges of reproducing its results.

AI trainingDeepSeekGPU Cost
0 likes · 16 min read
Why DeepSeek R1 Rivals ChatGPT o1: Architecture, Training, and Cost Insights
Architect
Architect
Feb 6, 2025 · Artificial Intelligence

DeepSeek‑R1: Reinforcement‑Learning‑Driven Long‑Chain Reasoning for Large Language Models

The article reviews DeepSeek‑R1, detailing its reinforcement‑learning‑based training pipeline that uses minimal supervised data, cold‑start fine‑tuning, multi‑stage RL, rejection‑sampling SFT, and distillation to achieve reasoning performance comparable to OpenAI‑o1‑1217, while also discussing successful contributions and failed experiments.

AI researchDeepSeek-R1LLM Reasoning
0 likes · 11 min read
DeepSeek‑R1: Reinforcement‑Learning‑Driven Long‑Chain Reasoning for Large Language Models
Architect
Architect
Feb 3, 2025 · Artificial Intelligence

How DeepSeek‑R1 Uses Pure Reinforcement Learning to Match OpenAI’s o1

This article presents DeepSeek‑R1 and DeepSeek‑R1‑Zero, two next‑generation LLMs trained with pure reinforcement learning and multi‑stage fine‑tuning, details their GRPO training framework, model‑distillation pipeline, open‑source release, and evaluation results that rival OpenAI’s o1‑1217 across reasoning, knowledge, and coding benchmarks.

DeepSeekLLM evaluationOpenAI o1
0 likes · 10 min read
How DeepSeek‑R1 Uses Pure Reinforcement Learning to Match OpenAI’s o1
Cognitive Technology Team
Cognitive Technology Team
Feb 3, 2025 · Artificial Intelligence

DeepSeek R1 Introduces Group‑Related Policy Optimization for Advanced Reasoning in Large Language Models

DeepSeek AI’s new open‑source model DeepSeek‑R1 leverages a novel Group‑Related Policy Optimization (GRPO) reinforcement‑learning framework and multi‑stage training to dramatically boost complex reasoning performance, achieving AIME 2024 Pass@1 scores comparable to OpenAI’s o1 model.

AIDeepSeekGRPO
0 likes · 4 min read
DeepSeek R1 Introduces Group‑Related Policy Optimization for Advanced Reasoning in Large Language Models
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 22, 2025 · Artificial Intelligence

Can RL‑Only Training Make LLMs Beat OpenAI‑o1? Inside DeepSeek‑R1’s Architecture and Results

DeepSeek‑R1’s open‑source series demonstrates that reinforcement‑learning‑only training can match top‑tier models like OpenAI‑o1, while a small amount of SFT further improves readability; the article dissects its technical report, training pipeline, reward design, distillation strategy, benchmark outcomes, and remaining challenges.

DeepSeekSupervised Fine‑Tuninglarge language model
0 likes · 11 min read
Can RL‑Only Training Make LLMs Beat OpenAI‑o1? Inside DeepSeek‑R1’s Architecture and Results
php Courses
php Courses
Jan 21, 2025 · Artificial Intelligence

Building Reinforcement Learning Algorithms with PHP

This article introduces reinforcement learning, explains its core concepts, and demonstrates how to implement a simple reinforcement learning algorithm in PHP using libraries such as Keras or TensorFlow, providing complete example code for environment and agent classes and outlining training and testing steps.

Code ExamplePHPartificial intelligence
0 likes · 5 min read
Building Reinforcement Learning Algorithms with PHP
Alimama Tech
Alimama Tech
Jan 8, 2025 · Artificial Intelligence

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

The paper introduces a model‑based reinforcement‑learning auto‑bidding framework that learns a neural‑network environment model from real logs, generates confidence‑aware virtual data fused with real data, and employs the COMBO+MICRO stabilizer and a Lagrange‑dual method for ROI‑constrained bidding, delivering up to 6.8 % higher consumption, 5 % GMV growth and 3.7 % ROI improvement on Alibaba’s platform.

auto-biddingbudget constrained biddingmodel-based RL
0 likes · 22 min read
Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising
DataFunSummit
DataFunSummit
Jan 5, 2025 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

The article presents a CIKM‑2024 paper that introduces MODRL‑TA, a multi‑objective deep reinforcement learning system combining multi‑objective Q‑learning, a cross‑entropy‑based decision‑fusion algorithm, and a progressive data‑augmentation pipeline to dynamically allocate search traffic on JD.com, with both offline and online experiments showing substantial gains in CTR, CVR, and overall platform performance.

Deep Learningcross-entropy methode‑commerce
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
JD Retail Technology
JD Retail Technology
Dec 26, 2024 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

MODRL‑TA is a multi‑objective deep reinforcement learning framework that unites independent Q‑learning agents, a cross‑entropy‑based decision‑fusion module, and progressive data‑augmentation to overcome cold‑start and multi‑objective trade‑offs in e‑commerce traffic allocation, delivering up to 18% more impressions, 4% higher CTR and 5% higher CVR in live tests.

Deep Learninge‑commercemulti-objective
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
Alimama Tech
Alimama Tech
Dec 17, 2024 · Artificial Intelligence

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

AuctionNet is a newly introduced benchmark that recreates a massive, realistic online advertising auction environment using latent diffusion‑generated traffic data, provides an 80 GB dataset of 5 × 10⁸ logs from 48 bidding agents, and offers baseline evaluations—including an Online LP that outperforms others—supporting thousands of fair NeurIPS 2024 competition submissions and open‑source tools for large‑scale game decision‑making research.

BenchmarkGenerative Modelsauto-bidding
0 likes · 15 min read
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
Kuaishou Tech
Kuaishou Tech
Dec 17, 2024 · Artificial Intelligence

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.

AdvertisingGenerative ModelsKuaishou
0 likes · 13 min read
NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 7, 2024 · Artificial Intelligence

What Is Reinforcement Fine-Tuning (RFT) and How Does It Supercharge LLMs?

Reinforcement Fine-Tuning (RFT) combines supervised fine‑tuning with reinforcement learning to teach large language models to reason more effectively, using separate training and validation datasets, graders, and PPO optimization, and has shown superior performance on tasks like gene prediction and math reasoning compared to standard SFT.

AIlarge language modelsmachine learning
0 likes · 8 min read
What Is Reinforcement Fine-Tuning (RFT) and How Does It Supercharge LLMs?
AI Product Manager Community
AI Product Manager Community
Dec 7, 2024 · Artificial Intelligence

How Reinforcement Fine-Tuning (RFT) Is Redefining AI Customization

Reinforcement Fine-Tuning (RFT), unveiled at OpenAI’s 12‑day launch, introduces a feedback‑loop approach that transforms generic models into specialized experts using reinforcement learning, small data, and domain‑specific scorers, offering product managers a powerful tool for rapid, cost‑effective AI customization across industries.

AI customizationFine-tuningmachine learning
0 likes · 7 min read
How Reinforcement Fine-Tuning (RFT) Is Redefining AI Customization
Bilibili Tech
Bilibili Tech
Dec 6, 2024 · Artificial Intelligence

Ensemble-based Offline-to-Online Reinforcement Learning (ENOTO): Methodology, Experiments, and Analysis

ENOTO introduces ensemble Q‑networks into the offline‑to‑online reinforcement‑learning pipeline, using minimum‑Q and uncertainty‑driven exploration to stabilize fine‑tuning, boost learning efficiency, and achieve 10‑25 % higher cumulative returns with minimal online interaction across MuJoCo and AntMaze benchmarks.

AntMazeENOTOEnsemble Q-Networks
0 likes · 16 min read
Ensemble-based Offline-to-Online Reinforcement Learning (ENOTO): Methodology, Experiments, and Analysis
Alimama Tech
Alimama Tech
Dec 4, 2024 · Artificial Intelligence

AIGB: Generative Auto‑Bidding via Diffusion Modeling

AIGB, introduced by Alibaba Mama in 2023, reframes large‑scale ad‑auction auto‑bidding as a generative sequence task using diffusion models, achieving up to 5 % GMV gains, improved stability and interpretability, and is now commercialized, open‑sourced, and featured in a NeurIPS‑endorsed competition.

AIGenerative Modelsauto-bidding
0 likes · 12 min read
AIGB: Generative Auto‑Bidding via Diffusion Modeling
Model Perspective
Model Perspective
Dec 3, 2024 · Artificial Intelligence

How Recommendation Algorithms Shape Our Habits—and What You Can Do About It

The article examines how recommendation algorithms reinforce user preferences, turning habits into stable feedback loops, and proposes mathematical models and practical strategies to introduce diversity and break behavioral fixation in the age of algorithmic personalization.

Diversitybehavioral modelinghabit formation
0 likes · 7 min read
How Recommendation Algorithms Shape Our Habits—and What You Can Do About It
DaTaobao Tech
DaTaobao Tech
Oct 30, 2024 · Artificial Intelligence

Understanding OpenAI o1: Chain‑of‑Thought, Scaling Laws, and Training Strategies

The article explains how OpenAI’s o1 model leverages chain‑of‑thought prompting, dual‑system cognitive theory, and new scaling laws—pre‑training on code/math and post‑training reinforcement with step‑wise reward models—to achieve superior reasoning, safety, and performance over GPT‑4, heralding a shift toward models that learn to think.

LLMSafetychain-of-thought
0 likes · 42 min read
Understanding OpenAI o1: Chain‑of‑Thought, Scaling Laws, and Training Strategies
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 21, 2024 · Artificial Intelligence

Unraveling RLHF: From PPO to DPO and Beyond – A Comprehensive Guide

This article provides a thorough, four‑part overview of RLHF for large language models, covering preference‑optimization algorithms (PPO‑based and offline RL approaches), reward‑model training techniques, inference‑time exploration strategies, and practical implementation details including the OpenRLHF framework and resource‑allocation tricks.

DPOLLM optimizationOpenRLHF
0 likes · 27 min read
Unraveling RLHF: From PPO to DPO and Beyond – A Comprehensive Guide
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 15, 2024 · Artificial Intelligence

How DPO Simplifies RLHF: A Deep Dive into Direct Preference Optimization

This article breaks down how Direct Preference Optimization (DPO) mathematically reduces the two‑stage RLHF pipeline into a single‑stage SFT process, explains the underlying loss transformations, and discusses DPO's practical limitations and trade‑offs for large language model alignment.

DPODirect Preference OptimizationRLHF
0 likes · 9 min read
How DPO Simplifies RLHF: A Deep Dive into Direct Preference Optimization
DataFunSummit
DataFunSummit
Sep 27, 2024 · Artificial Intelligence

Advances in Educational Large Language Models for Youth Programming and Personalized Learning

The presentation by Dr. Su Yu outlines challenges such as data sparsity and delayed learning effects in AI‑driven education, introduces three technical breakthroughs—domain‑specific LLM training, small‑knowledge learning via hierarchical knowledge graphs, and reinforcement‑based cognitive recommendation—and showcases product applications like the Frog Programming Platform, AI Programming Learning Machine, and digital‑human AI recorded courses.

AI educationKnowledge GraphPersonalized Learning
0 likes · 18 min read
Advances in Educational Large Language Models for Youth Programming and Personalized Learning
Model Perspective
Model Perspective
Sep 27, 2024 · Artificial Intelligence

Modeling Everyday Learning: From Reinforcement to Social Learning

The article explores how everyday decision‑making can be modeled using reinforcement learning and social learning frameworks, illustrating their strengths, limitations, and combined insights for understanding individual and collective behavior.

AIbehavioral modelingdecision making
0 likes · 8 min read
Modeling Everyday Learning: From Reinforcement to Social Learning
Architect
Architect
Sep 26, 2024 · Artificial Intelligence

Decoding OpenAI o1: How RL‑LLM Fusion Powers Next‑Gen Reasoning

This article provides a detailed technical analysis of OpenAI’s o1 model, exploring its enhanced logical reasoning, the likely use of reinforcement learning with hidden chain‑of‑thought generation, multi‑model architecture, training data pipelines, reward modeling, and how these innovations could reshape AI safety and scaling strategies.

AI SafetyLLMModel architecture
0 likes · 43 min read
Decoding OpenAI o1: How RL‑LLM Fusion Powers Next‑Gen Reasoning
JD Tech Talk
JD Tech Talk
Sep 23, 2024 · Artificial Intelligence

JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering

The JD Advertising R&D team applies cutting‑edge AI techniques—including query intent models, multimodal representation pipelines, reinforcement‑learning‑based auction mechanisms, generative recommendation with quantized product tokens, and large‑model infrastructure—to boost traffic valuation, ad relevance, revenue, and creative generation across the platform.

AIAdvertisinggraph neural networks
0 likes · 19 min read
JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering
Data Thinking Notes
Data Thinking Notes
Sep 13, 2024 · Artificial Intelligence

How OpenAI’s o1 Series Redefines Complex Reasoning and AI Safety

OpenAI’s new o1 series, including o1‑preview and o1‑mini, leverages reinforcement‑learning‑based chain‑of‑thought reasoning to achieve superior performance on academic exams, coding contests, and safety benchmarks, offering faster, cost‑effective options while advancing AI alignment and human‑preference evaluation.

AI SafetyBenchmarkOpenAI
0 likes · 15 min read
How OpenAI’s o1 Series Redefines Complex Reasoning and AI Safety
Tencent Advertising Technology
Tencent Advertising Technology
Aug 15, 2024 · Artificial Intelligence

Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding

This paper introduces RLLR, a label‑sensitive reward reinforcement learning method that improves natural language understanding tasks by aligning training objectives with label accuracy, and demonstrates its effectiveness across eight public NLU datasets and real‑world advertising feature evaluation, outperforming standard RLHF and SFT baselines.

AdvertisingRLHFlabel-sensitive reward
0 likes · 14 min read
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding
Tencent Advertising Technology
Tencent Advertising Technology
Aug 13, 2024 · Artificial Intelligence

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors

This paper investigates selection bias in large language models for multiple‑choice tasks, proposes metrics to quantify symbol‑content binding, introduces Reweighting Symbol‑Content Binding (RSCB) and Point‑wise Intelligent Feedback (PIF) methods, and demonstrates their effectiveness in reducing bias and improving accuracy, including a real‑world Tencent advertising feature‑evaluation deployment.

Symbol Bindingmultiple choicepointwise feedback
0 likes · 16 min read
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
Model Perspective
Model Perspective
Jul 31, 2024 · Artificial Intelligence

How Monte Carlo Tree Search Powers AlphaGo and Beyond: A Deep Dive

Monte Carlo Tree Search (MCTS) is a statistical heuristic algorithm that builds decision trees through selection, expansion, simulation, and backpropagation, enabling breakthroughs like AlphaGo’s victory and finding applications in robotics, autonomous driving, finance, and bioinformatics.

AI applicationsAlphaGoMCTS
0 likes · 7 min read
How Monte Carlo Tree Search Powers AlphaGo and Beyond: A Deep Dive
Model Perspective
Model Perspective
Jul 30, 2024 · Artificial Intelligence

Your Complete AI Learning Roadmap: From Basics to Large Model Mastery

This guide presents a comprehensive AI learning roadmap, dividing study into five progressive stages—from foundational math and programming to core deep‑learning and reinforcement‑learning techniques, large‑model training, industry applications, and future trends—plus curated book lists, tool recommendations, and practical RAG tutorials.

AI learning roadmapAI resourcesRAG
0 likes · 9 min read
Your Complete AI Learning Roadmap: From Basics to Large Model Mastery
Alimama Tech
Alimama Tech
Jul 29, 2024 · Artificial Intelligence

Generative Auto-bidding via Diffusion Modeling (AIGB)

The paper presents AIGB, a generative auto‑bidding framework that replaces reinforcement‑learning with a conditional diffusion model to generate optimal bidding trajectories, and demonstrates through offline benchmarks and Alibaba’s online A/B tests that it consistently outperforms RL baselines, boosting buy count, GMV, and ROI while maintaining low latency.

Generative ModelsMarketing AIauto-bidding
0 likes · 18 min read
Generative Auto-bidding via Diffusion Modeling (AIGB)
php Courses
php Courses
Jul 29, 2024 · Artificial Intelligence

Building Reinforcement Learning Algorithms with PHP

This article explains the fundamentals of reinforcement learning, demonstrates how PHP can be used with neural‑network libraries such as Keras or TensorFlow to implement a simple reinforcement‑learning agent, provides a complete PHP code example, and discusses its potential applications.

AICode Examplereinforcement learning
0 likes · 5 min read
Building Reinforcement Learning Algorithms with PHP
Kuaishou Tech
Kuaishou Tech
Jul 17, 2024 · Artificial Intelligence

Key Technical Innovations in Kuaishou’s “Kuaiyi” Large Model and Its Real-World Applications

The article details Kuaishou’s development of the 175B “Kuaiyi” multimodal large model, presenting eight novel technical innovations—from Temporal Scaling Law and MiLe Loss to MoE‑enhanced reward modeling—and describes how these advances enable high‑performance AI services such as the AI Xiao Kuai chatbot across diverse real‑world scenarios.

AI applicationsModel OptimizationMultimodal AI
0 likes · 12 min read
Key Technical Innovations in Kuaishou’s “Kuaiyi” Large Model and Its Real-World Applications
Alimama Tech
Alimama Tech
Jul 15, 2024 · Artificial Intelligence

Why Auto‑Bidding in Large‑Scale Auctions Is the Hottest NeurIPS Challenge

The article explains how NeurIPS ranks among top AI conferences, introduces the newly selected “Auto‑Bidding in Large‑Scale Auctions” competition, outlines its technical background, four generations of bidding strategies—from classic control to generative models—and details the competition’s tracks, rewards, and how researchers can participate.

AdvertisingNeurIPSauto-bidding
0 likes · 12 min read
Why Auto‑Bidding in Large‑Scale Auctions Is the Hottest NeurIPS Challenge
DataFunSummit
DataFunSummit
Jul 8, 2024 · Artificial Intelligence

World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview

This article reviews the role of world (mental) models and causal inference in reinforcement learning, covering their theoretical foundations, model‑based RL frameworks such as Dyna, sample‑efficiency challenges, causal structure learning, distribution correction, dynamics‑reward modeling, and experimental results that demonstrate performance gains across multiple tasks.

World Modelscausal inferencemodel-based RL
0 likes · 21 min read
World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview
Baobao Algorithm Notes
Baobao Algorithm Notes
May 30, 2024 · Artificial Intelligence

What’s the Latest RLHF Landscape? From PPO to ORPO Explained

This article surveys the current RLHF ecosystem, comparing on‑policy methods like PPO with off‑policy approaches such as DPO, and examines recent variants—including ReMax, GRPO, DPOP, TDPO, and ORPO—highlighting their algorithmic differences, resource trade‑offs, and practical performance insights.

AlignmentDPOLLM
0 likes · 23 min read
What’s the Latest RLHF Landscape? From PPO to ORPO Explained
Kuaishou Tech
Kuaishou Tech
May 27, 2024 · Artificial Intelligence

What Kuaishou’s Four ACL Papers Reveal About the Future of Large Language Models

The 62nd ACL conference accepted four papers from Kuaishou that explore multi‑turn instruction following, self‑agreement reasoning, fine‑grained reinforcement learning, and dynamic routing in Mixture‑of‑Experts models, each with detailed methods, experimental results, author lists, and public arXiv links.

ACL 2024Kuaishou ResearchMixture of Experts
0 likes · 11 min read
What Kuaishou’s Four ACL Papers Reveal About the Future of Large Language Models
NewBeeNLP
NewBeeNLP
May 13, 2024 · Artificial Intelligence

Why DPO Treats LLMs as Q‑Functions: A Deep Theoretical Dive

This article offers a detailed theoretical interpretation of the DPO algorithm, showing how large language models can be viewed as Q‑functions, unifying sequence‑wise and step‑wise decision perspectives, and discussing the resulting implications for reinforcement‑learning‑based alignment research.

DPOLLMQ-Function
0 likes · 14 min read
Why DPO Treats LLMs as Q‑Functions: A Deep Theoretical Dive
DataFunSummit
DataFunSummit
Apr 16, 2024 · Artificial Intelligence

Intelligent Risk Control: Definitions, Expert Systems, Algorithmic Systems, and Emerging AI Techniques

This article explains intelligent risk control as a synergy of expert experience and algorithmic decision‑making, outlines its definition, expert human systems, digital algorithmic systems, and explores advanced AI methods such as reinforcement learning, large language models with knowledge graphs, adversarial learning, graph neural networks, and a practical supply‑chain case study.

Graph Neural NetworkKnowledge Graphadversarial learning
0 likes · 11 min read
Intelligent Risk Control: Definitions, Expert Systems, Algorithmic Systems, and Emerging AI Techniques
JD Retail Technology
JD Retail Technology
Apr 15, 2024 · Artificial Intelligence

Design and Evolution of JD.com Recommendation Advertising Ranking Auction Mechanism

The article analyzes JD.com's recommendation advertising ranking auction mechanism, detailing its objectives, challenges in traffic value estimation, user interest exploration, and multi‑item auction fairness, and describing the technical evolution from traditional auctions to deep‑learning‑driven solutions.

Advertisingauctione‑commerce
0 likes · 18 min read
Design and Evolution of JD.com Recommendation Advertising Ranking Auction Mechanism
DataFunTalk
DataFunTalk
Apr 4, 2024 · Artificial Intelligence

Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework

This article reviews the challenges of textual‑only large language model interaction, introduces benchmark environments such as AFL World and ScienceWorld, compares baseline reinforcement‑learning approaches, and presents SwiftSage—a hybrid system that combines a fast T5‑based small model with a powerful LLM for planning and grounding, demonstrating superior performance, efficiency, and cost‑effectiveness while outlining current limitations and future research directions.

AISwiftSageinteractive agents
0 likes · 22 min read
Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework
DataFunSummit
DataFunSummit
Apr 2, 2024 · Artificial Intelligence

Reinforcement Learning: Fundamentals, Classic Algorithms, and Applications in Short Video Recommendation

This article provides an in-depth overview of reinforcement learning, covering its goals, mathematical foundations such as Markov Decision Processes, classic algorithms like DQN, and practical applications including short‑video recommendation systems that aim to improve user retention through RL‑based ranking.

DQNMarkov Decision ProcessRL applications
0 likes · 12 min read
Reinforcement Learning: Fundamentals, Classic Algorithms, and Applications in Short Video Recommendation
DataFunTalk
DataFunTalk
Mar 30, 2024 · Artificial Intelligence

Reinforcement Learning and Multi‑Task Recommendation: Two‑Stage Constrained Actor‑Critic and Multi‑Task RL Approaches at Kuaishou

This talk presents Kuaishou's research on combining reinforcement learning with multi‑task recommendation, detailing a two‑stage constrained actor‑critic method for short‑video ranking, a multi‑task RL framework, experimental results on offline and online systems, and practical Q&A insights.

Kuaishouactor-criticmulti-task recommendation
0 likes · 18 min read
Reinforcement Learning and Multi‑Task Recommendation: Two‑Stage Constrained Actor‑Critic and Multi‑Task RL Approaches at Kuaishou
Model Perspective
Model Perspective
Mar 8, 2024 · Artificial Intelligence

Master the Three Machine Learning Types and Model Paradigms

This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.

Discriminative ModelsGenerative ModelsUnsupervised Learning
0 likes · 13 min read
Master the Three Machine Learning Types and Model Paradigms
DataFunTalk
DataFunTalk
Mar 7, 2024 · Artificial Intelligence

Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework and Benchmark Analysis

This article reviews recent advances in using large language models for interactive embodied agents, introduces the SwiftSage dual‑model framework that combines a fast T5‑based small model with a powerful LLM for planning, evaluates it on benchmarks such as AFL World and ScienceWorld, and discusses efficiency, cost‑effectiveness, limitations, and future research directions.

AISwiftSageinteractive agents
0 likes · 23 min read
Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework and Benchmark Analysis
php Courses
php Courses
Feb 22, 2024 · Artificial Intelligence

Building Reinforcement Learning Algorithms with PHP

This article introduces reinforcement learning, explains its core concepts, and demonstrates how to implement a simple reinforcement learning algorithm in PHP using neural‑network libraries such as Keras, providing a complete code example that includes environment and agent classes.

Code ExamplePHPartificial intelligence
0 likes · 4 min read
Building Reinforcement Learning Algorithms with PHP
DaTaobao Tech
DaTaobao Tech
Jan 31, 2024 · Artificial Intelligence

Highlights of Recent AI Research Papers from Top Conferences (2023)

The article curates standout AI papers from 2023 CCF‑A conferences—including CVPR, ICLR, ACM MM, and INFORMS—showcasing advances such as Swin‑Transformer video quality assessment, cross‑modal e‑commerce product search, transformer‑based vehicle routing heuristics, diffusion‑driven dance generation, and reinforcement‑learning inventory replenishment.

AIComputer VisionMultimedia
0 likes · 23 min read
Highlights of Recent AI Research Papers from Top Conferences (2023)
DataFunSummit
DataFunSummit
Jan 27, 2024 · Artificial Intelligence

Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework

This article reviews recent advances in using large language models for embodied interactive agents, introduces the dual‑modality SwiftSage architecture that combines a fast T5‑based small model with a powerful large model for planning and grounding, and evaluates its performance on benchmarks such as ScienceWorld.

AI2BenchmarkPlanning
0 likes · 23 min read
Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework
DataFunTalk
DataFunTalk
Jan 25, 2024 · Artificial Intelligence

World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview

This article presents a detailed overview of world models and their role in reinforcement learning, explains how causal inference can enhance model-based RL, discusses sample efficiency challenges, and shares experimental findings and practical insights from recent research and industry applications.

AIcausal inferencemachine learning
0 likes · 22 min read
World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview
Alimama Tech
Alimama Tech
Jan 10, 2024 · Artificial Intelligence

Advances in Automated Bidding and Auction Mechanisms for Online Advertising

Advances in automated bidding for online ads have progressed from classic control and linear programming to reinforcement‑learning pipelines, offline and sustainable online RL, and finally generative‑model approaches, each enhancing decision strength, adaptability, and fairness while addressing simulation gaps, multi‑objective constraints, and real‑time efficiency.

Auction Designautomated biddinggenerative AI
0 likes · 25 min read
Advances in Automated Bidding and Auction Mechanisms for Online Advertising
DataFunSummit
DataFunSummit
Dec 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

This article presents a two‑stage constrained actor‑critic (TSCAC) algorithm that models short‑video recommendation as a constrained reinforcement‑learning problem, details its theoretical formulation and optimization loss, and validates its superiority through extensive offline and online experiments, followed by a multi‑task reinforcement‑learning framework (RMTL) that further improves multi‑objective recommendation performance.

Recommendation Systemsconstrained optimizationmulti-task learning
0 likes · 16 min read
Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework
Python Programming Learning Circle
Python Programming Learning Circle
Dec 16, 2023 · Artificial Intelligence

Using highway‑env with OpenAI Gym for Reinforcement Learning: Installation, Configuration, and DQN Training

This tutorial explains how to install the gym and highway‑env packages, configure the highway‑v0 environment, explore its observation types, and implement a DQN agent in Python to train and evaluate autonomous driving policies, complete with code snippets and performance visualizations.

DQNPythongym
0 likes · 9 min read
Using highway‑env with OpenAI Gym for Reinforcement Learning: Installation, Configuration, and DQN Training
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 8, 2023 · Artificial Intelligence

How BeautifulPrompt Automates Prompt Engineering for Text-to-Image Generation

BeautifulPrompt, presented at EMNLP 2023, introduces a deep generation model that automatically crafts high-quality prompts from simple image descriptions, enhancing text-to-image synthesis through data-driven fine‑tuning, reward modeling, and reinforcement learning techniques.

AI Generationreinforcement learningtext-to-image synthesis
0 likes · 8 min read
How BeautifulPrompt Automates Prompt Engineering for Text-to-Image Generation
Sohu Tech Products
Sohu Tech Products
Dec 6, 2023 · Artificial Intelligence

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

The paper introduces a real‑time controllable, multi‑objective re‑ranking framework for Taobao’s feed recommendation that combines actor‑critic reinforcement learning with hypernetworks to instantly adjust objective weights, handling diverse media and cold‑start constraints while delivering higher click‑through, diversity, and cold‑start ratios with only 20‑25 ms latency.

AlibabaReal-time ControlRecommendation Systems
0 likes · 34 min read
Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation
Kuaishou Tech
Kuaishou Tech
Dec 1, 2023 · Artificial Intelligence

Short Video Recommendation Algorithm Frontier Research Forum at CCIR 2023

The CCIR 2023 conference in Beijing, sponsored by Kuaishou, hosted a short‑video recommendation algorithm frontier research forum where over 100 experts and students shared the latest AI‑driven recommendation technologies, open datasets, and interdisciplinary challenges in short‑video platforms.

AIDatasetsconference
0 likes · 8 min read
Short Video Recommendation Algorithm Frontier Research Forum at CCIR 2023
Alimama Tech
Alimama Tech
Nov 28, 2023 · Artificial Intelligence

Evolution of Alibaba's AI-Driven Advertising Decision Technologies

The article traces Alibaba’s Alimama platform from classic control‑based bidding through linear programming and reinforcement‑learning approaches to generative‑AI‑driven strategies, detailing how deep‑learning models, offline and sustainable online RL frameworks, and large‑language‑model‑based bidding reshape automated auctions, fairness, and scalability in e‑commerce advertising.

AIAuction Designauto-bidding
0 likes · 38 min read
Evolution of Alibaba's AI-Driven Advertising Decision Technologies
21CTO
21CTO
Nov 27, 2023 · Artificial Intelligence

What Is the Mysterious Q* Model and Could It Redefine AI?

A speculative look at OpenAI's rumored Q* project explores its possible blend of Q‑learning and A* search, the potential for advanced logical reasoning, and the broader philosophical questions about AI consciousness, alignment, and the future of intelligent systems.

AI AlignmentAI consciousnessOpenAI
0 likes · 9 min read
What Is the Mysterious Q* Model and Could It Redefine AI?
Kuaishou Tech
Kuaishou Tech
Nov 23, 2023 · Artificial Intelligence

KuaiSim: A Comprehensive User Simulator for Reinforcement Learning in Recommendation Systems

KuaiSim is a comprehensive user simulation environment for recommendation systems that models immediate, long‑term, and cross‑session feedback, supports list‑wise, whole‑session, and retention tasks, provides baselines and evaluation metrics, and demonstrates superior performance on KuaiRand and ML‑1M datasets.

BenchmarkKuaiSimUser Simulation
0 likes · 14 min read
KuaiSim: A Comprehensive User Simulator for Reinforcement Learning in Recommendation Systems
DataFunSummit
DataFunSummit
Nov 21, 2023 · Artificial Intelligence

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

This article presents an in‑depth overview of Tencent's TRS automatic hyperparameter tuning, covering background, challenges, the evolution from Bayesian optimization to evolution strategies and reinforcement learning, a systematic platform solution, real‑world deployment results, and a Q&A session.

Bayesian OptimizationEvolution StrategiesOnline Learning
0 likes · 20 min read
Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice
Kuaishou Tech
Kuaishou Tech
Nov 21, 2023 · Artificial Intelligence

Kuaishou Academic Forum on Cutting-Edge Short Video Recommendation Algorithms (Nov 23, 2023)

The Kuaishou Academic Forum held on November 23 in Beijing presented cutting‑edge research on short‑video recommendation algorithms, featuring talks on reinforcement learning, user interest modeling, graph neural networks, and a comprehensive recommender‑system simulator, while also offering registration details and a brief company overview.

Kuaishouacademic forumgraph neural networks
0 likes · 5 min read
Kuaishou Academic Forum on Cutting-Edge Short Video Recommendation Algorithms (Nov 23, 2023)
DataFunTalk
DataFunTalk
Nov 14, 2023 · Artificial Intelligence

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

This article presents a comprehensive study of a controllable multi‑objective re‑ranking model for Taobao's information‑flow recommendation, detailing the challenges of complex feed scenarios, three modeling paradigms (V1‑V3), an actor‑critic reinforcement learning framework with hypernet‑generated weights, and extensive online evaluation results.

Real-time ControlRecommendation Systemshypernetworks
0 likes · 31 min read
Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed
Sohu Tech Products
Sohu Tech Products
Nov 8, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

The presentation introduces a two‑stage constrained actor‑critic algorithm that learns auxiliary policies for interaction signals before optimizing watch‑time under KL constraints, and a reinforcement‑learning multi‑task learning framework that models session‑level dynamics with adaptive multi‑critic weighting, both achieving significant offline and online gains in short‑video recommendation.

Recommendation Systemsactor-criticconstrained optimization
0 likes · 16 min read
Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework
DataFunTalk
DataFunTalk
Nov 6, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

This article presents a two‑stage constrained actor‑critic reinforcement learning algorithm for short‑video recommendation, models the problem as a constrained MDP, details the algorithm’s stages, and reports extensive offline and online experiments showing superior watch‑time and interaction metrics, followed by a multi‑task RL framework and its evaluations.

Recommendation Systemsconstrained optimizationmulti‑task learning
0 likes · 16 min read
Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework
NetEase Smart Enterprise Tech+
NetEase Smart Enterprise Tech+
Oct 19, 2023 · Artificial Intelligence

Unleashing Game AI: Inside NetEase’s Bray Distributed RL Framework

NetEase’s AI team reveals how their self‑developed distributed reinforcement‑learning platform, Bray, enables high‑level AI agents for the MOBA game Dream of Kingdom 2, covering GameCore integration, weighted random initialization, modular APIs, difficulty scaling, and cost‑effective training for realistic player experiences.

AI FrameworkDistributed TrainingMoBA
0 likes · 9 min read
Unleashing Game AI: Inside NetEase’s Bray Distributed RL Framework
Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 18, 2023 · Artificial Intelligence

Design and Implementation of a Home‑Page Recommendation System Using Reinforcement Learning and DPP

This article presents a comprehensive design for Zhuanzhuan's home‑page recommendation pipeline, detailing the system architecture, challenges of traffic efficiency and diversity, and a two‑stage solution that applies Proximal Policy Optimization reinforcement learning in the re‑ranking module and Determinantal Point Process optimization in the coarse‑ranking and traffic‑pool stages, followed by offline simulation, online deployment, and evaluation metrics.

DPPmachine learningranking
0 likes · 18 min read
Design and Implementation of a Home‑Page Recommendation System Using Reinforcement Learning and DPP
Alimama Tech
Alimama Tech
Oct 11, 2023 · Artificial Intelligence

How Minimax Regret Optimization Tackles Black‑Box Adversarial Bidding Constraints

This article explains how the Alibaba‑Mama team addresses constrained ROI bidding in a black‑box adversarial environment by introducing a Minimax Regret Optimization framework that aligns training and test distributions, builds a causal world model, and demonstrates robust performance on synthetic and real‑world ad auctions.

adversarial biddingconstrained optimizationminimax regret
0 likes · 14 min read
How Minimax Regret Optimization Tackles Black‑Box Adversarial Bidding Constraints
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 9, 2023 · Artificial Intelligence

Demystifying RLHF and PPO for Large Language Models: Theory and Practice

This article explains why Reinforcement Learning from Human Feedback (RLHF) is crucial for LLM intelligence, outlines the three-stage training pipeline, details InstructGPT's reward model and PPO optimization, and provides a practical guide to implementing RLHF with deep‑learning frameworks.

PPORLHFReward Modeling
0 likes · 17 min read
Demystifying RLHF and PPO for Large Language Models: Theory and Practice
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Sep 13, 2023 · Artificial Intelligence

How Pai‑Megatron‑Patch Accelerates Large Language Model Training on Alibaba Cloud

This article introduces Pai‑Megatron‑Patch, an open‑source tool from Alibaba Cloud that streamlines large language model (LLM) training, weight conversion, FP8 mixed‑precision acceleration, and reinforcement‑learning workflows, providing detailed architecture, key features, code examples, and step‑by‑step usage instructions.

FP8LLM trainingMegatron
0 likes · 19 min read
How Pai‑Megatron‑Patch Accelerates Large Language Model Training on Alibaba Cloud
Alimama Tech
Alimama Tech
Aug 23, 2023 · Artificial Intelligence

Reinforcement Learning for Pacing in Preloaded Ads (RLTP)

The paper introduces RLTP, a reinforcement‑learning‑based pacing system that models delayed‑impression preloaded ads as an MDP, uses a dueling DQN to select traffic probabilities, and simultaneously meets exposure targets, ensures smooth delivery, and maximizes CTR, outperforming rule‑based and PID baselines while removing complex multi‑stage pipelines.

RLTPad pacingdelayed impression
0 likes · 16 min read
Reinforcement Learning for Pacing in Preloaded Ads (RLTP)
ByteDance SE Lab
ByteDance SE Lab
Aug 21, 2023 · Artificial Intelligence

How Fastbot Uses Reinforcement Learning for Faster Android GUI Testing

Fastbot is a reusable, model‑based Android GUI testing tool that leverages reinforcement‑learning techniques to learn from previous test runs, accelerating coverage and crash detection through a two‑phase workflow, probabilistic and learning‑based event selection, and provides configurable custom events, widget blocking, and tree‑pruning features.

GUI automationandroid testingfastbot
0 likes · 16 min read
How Fastbot Uses Reinforcement Learning for Faster Android GUI Testing
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 20, 2023 · Artificial Intelligence

What Is RLHF? Benefits, Limits, and Design Tips for Human‑Feedback Reinforcement Learning

This article explains Reinforcement Learning with Human Feedback (RLHF), outlining its definition, suitable tasks, advantages over other reward‑model methods, types of algorithms, challenges of human feedback, and practical strategies to mitigate its limitations for building robust AI systems.

AI AlignmentHuman FeedbackReward Modeling
0 likes · 14 min read
What Is RLHF? Benefits, Limits, and Design Tips for Human‑Feedback Reinforcement Learning
Alimama Tech
Alimama Tech
Aug 16, 2023 · Artificial Intelligence

Personalized Automated Bidding Framework (PerBid) for Fairness‑Aware Online Advertising

PerBid introduces a personalized automated bidding framework that creates context‑aware RL agents for advertiser clusters using a profiling network to embed static and dynamic campaign features, and experiments on Alibaba’s display‑ad platform show up to 10.85% performance gains while markedly improving fairness across heterogeneous advertisers.

Fairnessautomated biddingonline advertising
0 likes · 23 min read
Personalized Automated Bidding Framework (PerBid) for Fairness‑Aware Online Advertising
Baidu Geek Talk
Baidu Geek Talk
Aug 16, 2023 · Artificial Intelligence

Understanding Reinforcement Learning: From Basics to PPO and Policy Gradient

This article provides a comprehensive overview of reinforcement learning, covering fundamental concepts, differences from supervised learning, algorithm families, policy gradient methods, practical tricks like baselines and reward‑to‑go, and detailed explanations of TRPO and PPO variants with illustrative diagrams.

PPOactor-criticmachine learning
0 likes · 19 min read
Understanding Reinforcement Learning: From Basics to PPO and Policy Gradient
DataFunTalk
DataFunTalk
Aug 7, 2023 · Artificial Intelligence

DataFun Decision Intelligence Summit – Reinforcement Learning Forum Overview

The DataFun Decision Intelligence Summit brings together leading researchers and industry experts to present cutting‑edge reinforcement learning algorithms, safety considerations, distributional methods, and real‑world applications such as vehicle routing, recommender systems, and power‑grid scheduling, highlighting future directions and audience takeaways.

AIRecommendation Systemsdistributional RL
0 likes · 12 min read
DataFun Decision Intelligence Summit – Reinforcement Learning Forum Overview
Meituan Technology Team
Meituan Technology Team
Jul 20, 2023 · Artificial Intelligence

Novelty Recommendation for Meituan Food Delivery: System Design, Challenges, and Solutions

Meituan’s food‑delivery team built a novelty‑focused recommendation pipeline—combining dual‑tower recall, novelty‑aware ranking, personalized mixed‑ranking weights, and reinforcement‑learning insertion—to surface merchants unseen by users, achieving 19% higher exposure novelty, 25% more order novelty, and improved ratings while keeping RPM loss under 0.5%.

food deliverynoveltyranking
0 likes · 28 min read
Novelty Recommendation for Meituan Food Delivery: System Design, Challenges, and Solutions
DataFunSummit
DataFunSummit
Jun 19, 2023 · Artificial Intelligence

Overview of Decision Intelligence and Reinforcement Learning

This article provides a comprehensive overview of decision intelligence, distinguishing predictive and decision tasks, classifies decision environments, and delves into reinforcement learning fundamentals, algorithms such as SARSA, deep reinforcement learning, and discusses current applications, challenges, and future research directions.

artificial intelligencedecision intelligenceoptimization
0 likes · 12 min read
Overview of Decision Intelligence and Reinforcement Learning
DaTaobao Tech
DaTaobao Tech
Jun 9, 2023 · Artificial Intelligence

Generator-Evaluator Architecture for End-to-End Re-ranking in Information Flow

The paper introduces a Generator‑Evaluator (GE) architecture that end‑to‑end re‑ranks information‑flow items using a pointer‑network seq2seq generator and a reward‑estimating evaluator, jointly optimizing relevance and business utilities such as diversity, traffic control, inter‑group ordering, and fixed‑slot insertion, achieving over 70% better‑percentage and significant online gains on Taobao.

Information Flowgenerator-evaluatorranking
0 likes · 19 min read
Generator-Evaluator Architecture for End-to-End Re-ranking in Information Flow
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 9, 2023 · Artificial Intelligence

2023 NIRC PhD Graduates Reveal Cutting-Edge AI and Network Intelligence Research

In 2023 the Network Intelligent Research Center celebrated its largest PhD graduating class—seven scholars whose dissertations span deep‑vision hand‑gesture estimation, multi‑scenario network transmission, graph alignment, interactive streaming, knowledge‑defined networking, wireless body‑area networking, and more—showcasing significant AI‑driven advances and high‑impact publications.

Computer VisionDeep LearningGraph Alignment
0 likes · 30 min read
2023 NIRC PhD Graduates Reveal Cutting-Edge AI and Network Intelligence Research
Didi Tech
Didi Tech
May 23, 2023 · Artificial Intelligence

Driver‑Passenger Matching in Didi’s Ride‑Hailing Market: Algorithms and Techniques

The article surveys Didi’s driver‑passenger matching challenges and presents a suite of solutions—from greedy nearest‑driver and Kuhn‑Munkres bipartite matching to stable marriage, dynamic and one‑to‑many assignments, reinforcement‑learning, routing and queueing models—while validating assumptions statistically, integrating preference‑aware machine learning, and outlining multi‑objective and digital‑twin future research.

Ride Hailingalgorithmmatching
0 likes · 23 min read
Driver‑Passenger Matching in Didi’s Ride‑Hailing Market: Algorithms and Techniques
DataFunTalk
DataFunTalk
May 20, 2023 · Artificial Intelligence

Understanding Didi’s Online Marketplace: Core Concepts, Technical Challenges, and Emerging Technologies

This article introduces Didi’s real‑time online marketplace, explains its fundamental principles, network effects, and social efficiency benefits, and examines key technical areas such as mechanism design, decision intelligence, operations research, reinforcement learning, and causal inference that drive its advanced matching and dispatch strategies.

Operations Researchartificial intelligencedecision intelligence
0 likes · 16 min read
Understanding Didi’s Online Marketplace: Core Concepts, Technical Challenges, and Emerging Technologies
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 8, 2023 · Artificial Intelligence

Understanding the Principles Behind ChatGPT: NLP, Transformers, and Reinforcement Learning

This article explains how ChatGPT works by covering the fundamentals of natural language processing, generative language models, deep learning, the Transformer architecture, attention mechanisms, few‑shot learning, and the reinforcement‑learning techniques that align its outputs with human preferences.

AIChatGPTNLP
0 likes · 24 min read
Understanding the Principles Behind ChatGPT: NLP, Transformers, and Reinforcement Learning
Kuaishou Tech
Kuaishou Tech
Apr 29, 2023 · Artificial Intelligence

RMTL: A Reinforcement Learning Based Multi‑Task Learning Framework for Session‑Level Recommendation

The paper proposes RMTL, a reinforcement‑learning driven multi‑task learning framework that builds session‑level MDPs, trains a multi‑task actor‑critic network with dynamic loss weighting, and demonstrates significant AUC improvements over state‑of‑the‑art MTL recommendation models on public datasets.

actor‑criticadaptive loss weightingmulti-task learning
0 likes · 8 min read
RMTL: A Reinforcement Learning Based Multi‑Task Learning Framework for Session‑Level Recommendation
Kuaishou Tech
Kuaishou Tech
Apr 28, 2023 · Artificial Intelligence

How Hyper‑Actor Critic Redefines Reinforcement Learning for Recommendation Systems

This article presents the Hyper‑Actor Critic (HAC) framework that splits reinforcement‑learning policies into continuous hyper‑actions and effective recommendation lists, introduces alignment and supervised losses, and demonstrates superior performance on an online simulator compared to existing RL and supervised methods.

AI researchRecommendation Systemshyper-actor critic
0 likes · 9 min read
How Hyper‑Actor Critic Redefines Reinforcement Learning for Recommendation Systems
Kuaishou Tech
Kuaishou Tech
Apr 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor‑Critic (TSCAC) for Short‑Video Recommendation

The paper models short‑video recommendation as a constrained Markov decision process and introduces a two‑stage constrained actor‑critic algorithm that jointly maximizes watch time while satisfying multiple interaction constraints, demonstrating superior offline and online performance on the KuaiRand dataset and Kuaishou app.

actor-criticconstrained optimizationoffline evaluation
0 likes · 7 min read
Two-Stage Constrained Actor‑Critic (TSCAC) for Short‑Video Recommendation
Kuaishou Tech
Kuaishou Tech
Apr 22, 2023 · Artificial Intelligence

Reinforcement Learning for User Retention (RLUR) in Short Video Recommendation Systems

This paper presents RLUR, a reinforcement‑learning algorithm that models user‑retention optimization as an infinite‑horizon request‑based Markov Decision Process, addressing uncertainty, bias, and delayed reward challenges to directly improve retention, DAU, and engagement in short‑video recommendation platforms.

KuaishouRLURUser Retention
0 likes · 8 min read
Reinforcement Learning for User Retention (RLUR) in Short Video Recommendation Systems
Alimama Tech
Alimama Tech
Apr 3, 2023 · Artificial Intelligence

AI-Generated Bidding (AIGB): Using Generative Models for Automated Advertising Bidding

AI‑Generated Bidding (AIGB) replaces reinforcement‑learning with a conditional generative model that learns the joint distribution of bids, objectives and constraints from historical trajectories, enabling interpretable, diverse, constraint‑aware bidding strategies that improve efficiency, scalability and explainability for large‑scale advertising platforms.

automated biddingconditional modelinggenerative AI
0 likes · 15 min read
AI-Generated Bidding (AIGB): Using Generative Models for Automated Advertising Bidding
Kuaishou Tech
Kuaishou Tech
Mar 29, 2023 · Artificial Intelligence

ResAct: A Reinforcement Learning Approach for Long-Term User Retention in Sequential Recommendation

The paper introduces ResAct, a reinforcement‑learning framework that improves long‑term user retention in sequential recommendation by constraining the policy space near the online‑serving policy and employing a conditional variational auto‑encoder, residual actor, and state‑action value network, achieving significant gains over existing methods on a large‑scale short‑video dataset.

ResActUser Retentionreinforcement learning
0 likes · 9 min read
ResAct: A Reinforcement Learning Approach for Long-Term User Retention in Sequential Recommendation
NetEase Smart Enterprise Tech+
NetEase Smart Enterprise Tech+
Mar 27, 2023 · Artificial Intelligence

How Reinforcement Learning Powers AI Bots in ‘Barbarian Battle 2’

This article details NetEase Zhiji and Dianhun Network's use of reinforcement learning, a distributed training framework, and middleware to create, train, deploy, and iterate AI robots for the game "Barbarian Battle 2", highlighting technical challenges, solutions, and the impact on player experience.

AI botsDistributed TrainingGame Development
0 likes · 13 min read
How Reinforcement Learning Powers AI Bots in ‘Barbarian Battle 2’
Python Programming Learning Circle
Python Programming Learning Circle
Mar 10, 2023 · Artificial Intelligence

Google's i‑S2R and GoalsEye: Robot Table‑Tennis Learning from Human Interaction

The article explains how Google's i‑S2R and GoalsEye projects use iterative simulation‑to‑real training, behavior cloning and goal‑conditioned learning to enable robots to play table‑tennis with humans, highlighting the challenges, experimental setup, and performance improvements achieved across player skill levels.

AI researchRoboticsbehavior cloning
0 likes · 6 min read
Google's i‑S2R and GoalsEye: Robot Table‑Tennis Learning from Human Interaction
Top Architect
Top Architect
Mar 10, 2023 · Artificial Intelligence

Understanding InstructGPT and ChatGPT: Architecture, Training Pipeline, and Performance Analysis

This article provides a comprehensive overview of the GPT series, explains the differences between prompt learning and instruction learning, details the three‑stage training pipeline of InstructGPT/ChatGPT—including supervised fine‑tuning, reward‑model training, and PPO‑based reinforcement learning—examines their strengths, weaknesses, and future research directions, and discusses the broader impact of these models on AI development.

AIChatGPTGPT
0 likes · 22 min read
Understanding InstructGPT and ChatGPT: Architecture, Training Pipeline, and Performance Analysis
21CTO
21CTO
Feb 23, 2023 · Artificial Intelligence

How Does ChatGPT Really Work? Inside the RLHF Training Process

This article explains ChatGPT’s architecture, the distinction between model capability and consistency, how next‑token and masked‑language‑model training lead to inconsistencies, and how OpenAI’s supervised fine‑tuning, reward‑model training, and PPO reinforcement learning (RLHF) are combined to improve alignment while highlighting the method’s limitations.

AI AlignmentChatGPTRLHF
0 likes · 15 min read
How Does ChatGPT Really Work? Inside the RLHF Training Process
IT Architects Alliance
IT Architects Alliance
Feb 23, 2023 · Artificial Intelligence

Training a Positive Review Generator with RLHF and PPO

This article demonstrates how to use Reinforcement Learning from Human Feedback (RLHF) with a PPO algorithm and a sentiment‑analysis model to train a language model that generates positive product reviews, covering task definition, data sampling, reward evaluation, model optimization, and experimental results.

GPTLanguage ModelPPO
0 likes · 11 min read
Training a Positive Review Generator with RLHF and PPO